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01.12.2015 | Research article | Ausgabe 1/2016 Open Access

BMC Public Health 1/2016

Spatial analysis of HIV infection and associated individual characteristics in Burundi: indications for effective prevention

Zeitschrift:
BMC Public Health > Ausgabe 1/2016
Autoren:
Emmanuel Barankanira, Nicolas Molinari, Théodore Niyongabo, Christian Laurent
Wichtige Hinweise

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

E.B., N.M., and C.L. elaborated the study design. E.B. obtained the permission to use the 2010 Burundi DHS data, performed the statistical analysis and, with C.L., wrote the first draft of the paper. E.B., N.M., T.N. and C.L. contributed to the interpretation of the results, reviewed the draft, and approved the final manuscript.

Abstract

Background

Adequate resource allocation is critical in the battle against HIV/AIDS, especially in Africa. The determination of the location and nature of HIV services to implement must comply with the geographic, social and behavioral characteristics of patients. We therefore investigated the spatial heterogeneity of HIV prevalence in Burundi and then assessed the association of social and behavioral characteristics with HIV infection accounting for the spatial heterogeneity.

Methods

We used data from the 2010 Demographic and Health Survey. We analyzed these data with a geostatistical approach (which takes into account spatial autocorrelation) by i) interpolating HIV data using the kernel density estimation, ii) identifying the spatial clusters with high and low HIV prevalence using the Kulldorff spatial scan statistics, and then iii) performing a multivariate spatial logistic regression.

Results

Overall HIV prevalence was 1.4 %. The interpolated data showed the great spatial heterogeneity of HIV prevalence (from 0 to 10 %), independently of administrative boundaries. A cluster with high HIV prevalence was found in the capital city and adjacent areas (3.9 %; relative risk 3.7, p < 0.001) whereas a cluster with low prevalence straddled two southern provinces (0 %; p = 0.02). By multivariate spatial analysis, HIV infection was significantly associated with the female sex (posterior odds ratio [POR] 1.36, 95 % credible interval [CrI] 1.13-1.64), an older age (POR 1.97, 95 % CrI 1.26-3.08), the level of education (POR 1.50, 95 % CrI 1.22-1.84), the marital status (POR 1.86, 95 % CrI 1.23-2.80), a higher wealth index (POR 2.11, 95 % CrI 1.77-2.51), the sexual activity (POR 1.76, 95 % CrI 1.04-2.96), and a history of sexually transmitted infection (POR 2.03, 95 % CrI 1.56-2.64).

Conclusions

Our study, which shows where and towards which populations HIV resources should be allocated, could help national health policy makers develop an effective HIV intervention in Burundi. Our findings support the strategy of the Joint United Nations Programme on HIV/AIDS (UNAIDS) for country-specific, in-depth analyses of HIV epidemics to tailor national prevention responses.
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